"""simple docstring"""







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Union[str, Any] ):
     return 1.0 / (1.0 + np.exp(-_outputs ))



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any ):
     lowercase_      :							int						=      np.max(_outputs	,     axis=-1	,     keepdims=UpperCAmelCase_ )
     lowercase_      :							Any						=      np.exp(_outputs - maxes )
     return shifted_exp / shifted_exp.sum(axis=-1	,     keepdims=UpperCAmelCase_ )







class 			UpperCamelCase						(     _lowerCAmelCase    ):
   lowercase				=    '''sigmoid'''
   lowercase				=    '''softmax'''
   lowercase				=    '''none'''







@add_end_docstrings(
    _lowerCAmelCase		, r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '		, )
class 			UpperCamelCase						(     _lowerCAmelCase    ):
   lowercase				=    False
   lowercase				=    ClassificationFunction.NONE
   def __init__(     self	,**__UpperCamelCase     )      -> Dict:


        '''simple docstring'''


        super().__init__(**UpperCAmelCase_     )

        self.check_model_type(
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase=None	,__UpperCamelCase=None	,__UpperCamelCase=""	,**__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        lowercase_      :							List[str]						=      tokenizer_kwargs

        lowercase_      :							Any						=      {}
        if hasattr(self.model.config	,'return_all_scores'     ) and return_all_scores is None:
             lowercase_      :							Tuple						=      self.model.config.return_all_scores

        if isinstance(UpperCAmelCase_	,UpperCAmelCase_     ) or top_k is None:
             lowercase_      :							Optional[Any]						=      top_k
             lowercase_      :							str						=      False
        elif return_all_scores is not None:
             warnings.warn(
                 '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                 ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'	,UpperCAmelCase_	,)
             if return_all_scores:
                  lowercase_      :							Tuple						=      None
             else:
                  lowercase_      :							Dict						=      1

        if isinstance(UpperCAmelCase_	,UpperCAmelCase_     ):
             lowercase_      :							List[str]						=      ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
             lowercase_      :							Optional[Any]						=      function_to_apply
        return preprocess_params, {}, postprocess_params
   def __call__(     self	,*__UpperCamelCase	,**__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        lowercase_      :							str						=      super().__call__(*UpperCAmelCase_	,**UpperCAmelCase_     )
        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
        lowercase_      :							List[str]						=      'top_k' not in kwargs
        if isinstance(args[0]	,UpperCAmelCase_     ) and _legacy:
             # This pipeline is odd, and return a list when single item is run
             return [result]
        else:
             return result
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,**__UpperCamelCase     )      -> Dict[str, GenericTensor]:


        '''simple docstring'''


        lowercase_      :							Tuple						=      self.framework
        if isinstance(UpperCAmelCase_	,UpperCAmelCase_     ):
             return self.tokenizer(**UpperCAmelCase_	,return_tensors=UpperCAmelCase_	,**UpperCAmelCase_     )
        elif isinstance(UpperCAmelCase_	,UpperCAmelCase_     ) and len(UpperCAmelCase_     ) == 1 and isinstance(inputs[0]	,UpperCAmelCase_     ) and len(inputs[0]     ) == 2:
             # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
             return self.tokenizer(
                 text=inputs[0][0]	,text_pair=inputs[0][1]	,return_tensors=UpperCAmelCase_	,**UpperCAmelCase_     )
        elif isinstance(UpperCAmelCase_	,UpperCAmelCase_     ):
             # This is likely an invalid usage of the pipeline attempting to pass text pairs.
             raise ValueError(
                 'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                 ' dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.'     )
        return self.tokenizer(UpperCAmelCase_	,return_tensors=UpperCAmelCase_	,**UpperCAmelCase_     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> int:


        '''simple docstring'''


        return self.model(**UpperCAmelCase_     )







   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase=None	,__UpperCamelCase=1	,__UpperCamelCase=True     )      -> List[str]:


        '''simple docstring'''


        if function_to_apply is None:
             if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                  lowercase_      :							List[str]						=      ClassificationFunction.SIGMOID
             elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                  lowercase_      :							List[Any]						=      ClassificationFunction.SOFTMAX
             elif hasattr(self.model.config	,'function_to_apply'     ) and function_to_apply is None:
                  lowercase_      :							Optional[Any]						=      self.model.config.function_to_apply
             else:
                  lowercase_      :							Optional[Any]						=      ClassificationFunction.NONE

        lowercase_      :							Any						=      model_outputs['logits'][0]
        lowercase_      :							List[Any]						=      outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
             lowercase_      :							List[Any]						=      sigmoid(UpperCAmelCase_     )
        elif function_to_apply == ClassificationFunction.SOFTMAX:
             lowercase_      :							Tuple						=      softmax(UpperCAmelCase_     )
        elif function_to_apply == ClassificationFunction.NONE:
             lowercase_      :							Any						=      outputs
        else:
             raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''     )

        if top_k == 1 and _legacy:
             return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

        lowercase_      :							int						=      [
            {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCAmelCase_     )
        ]
        if not _legacy:
             dict_scores.sort(key=lambda __UpperCamelCase     : x["score"]	,reverse=UpperCAmelCase_     )
             if top_k is not None:
                  lowercase_      :							int						=      dict_scores[:top_k]
        return dict_scores



"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





